Search results for: mathematical modeling.

Authors: H. Khanfari, M. Johari Fard

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Dealing with carbonate reservoirs can be mind-boggling for the reservoir engineers due to various digenetic processes that cause a variety of properties through the reservoir. A good estimation of the reservoir heterogeneity which is defined as the quality of variation in rock properties with location in a reservoir or formation, can better help modeling the reservoir and thus can offer better understanding of the behavior of that reservoir. Most of reservoirs are heterogeneous formations whose mineralogy, organic content, natural fractures, and other properties vary from place to place. Over years, reservoir engineers have tried to establish methods to describe the heterogeneity, because heterogeneity is important in modeling the reservoir flow and in well testing. Geological methods are used to describe the variations in the rock properties because of the similarities of environments in which different beds have deposited in. To illustrate the heterogeneity of a reservoir vertically, two methods are generally used in petroleum work: Dykstra-Parsons permeability variations (V) and Lorenz coefficient (L) that are reviewed briefly in this paper. The concept of Lorenz is based on statistics and has been used in petroleum from that point of view. In this paper, we correlated the statistical-based Lorenz method to a petroleum concept, i.e. Kozeny-Carman equation and derived the straight line plot of Lorenz graph for a homogeneous system. Finally, we applied the two methods on a heterogeneous field in South Iran and discussed each, separately, with numbers and figures. As expected, these methods show great departure from homogeneity. Therefore, for future investment, the reservoir needs to be treated carefully.

Keywords: Carbonate reservoirs, heterogeneity, homogeneous system, Dykstra-Parsons permeability variations (V), Lorenz coefficient (L).

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Authors: Somayeh Komeylian

Abstract:

The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, adaptive antenna array, Deep Neural Network, LS-SVM optimization model, radial basis function, MSE.

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Authors: Omid Ghasemalizadeh, Mohammad Reza Mirzaee, Hossein Sadeghi, Mohammad Taghi Ahmadian

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There are lots of different ways to find the natural frequencies of a rotating system. One of the most effective methods which is used because of its precision and correctness is the application of the transfer matrix. By use of this method the entire continuous system is subdivided and the corresponding differential equation can be stated in matrix form. So to analyze shaft that is this paper issue the rotor is divided as several elements along the shaft which each one has its own mass and moment of inertia, which this work would create possibility of defining the named matrix. By Choosing more elements number, the size of matrix would become larger and as a result more accurate answers would be earned. In this paper the dynamics of a rotor-bearing system is analyzed, considering the gyroscopic effect. To increase the accuracy of modeling the thickness of the disk and bearings is also taken into account which would cause more complicated matrix to be solved. Entering these parameters to our modeling would change the results completely that these differences are shown in the results. As said upper, to define transfer matrix to reach the natural frequencies of probed system, introducing some elements would be one of the requirements. For the boundary condition of these elements, bearings at the end of the shaft are modeled as equivalent spring and dampers for the discretized system. Also, continuous model is used for the shaft in the system. By above considerations and using transfer matrix, exact results are taken from the calculations. Results Show that, by increasing thickness of the bearing the amplitude of vibration would decrease, but obviously the stiffness of the shaft and the natural frequencies of the system would accompany growth. Consequently it is easily understood that ignoring the influences of bearing and disk thicknesses would results not real answers.

Keywords: Rotor System, Disk and Bearing Thickness, Transfer Matrix, Amplitude.

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Authors: Z. Harouni, L. Osman, M. Yeddes, A. Gharsallah, H. Baudrand

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In this paper, an efficient wave concept iterative process (WCIP) with auxiliary Sources is presented for full wave investigation of an active microwave structure on micro strip technology. Good agreement between the experimental and simulation results is observed.

Keywords: WCIP, Dual-Gate Transistor, Auxiliary source.

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Authors: Karam Y. Maalawi, Hanan E.M EL-Sayed

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This paper presents an exact analytical model for optimizing stability of thin-walled, composite, functionally graded pipes conveying fluid. The critical flow velocity at which divergence occurs is maximized for a specified total structural mass in order to ensure the economic feasibility of the attained optimum designs. The composition of the material of construction is optimized by defining the spatial distribution of volume fractions of the material constituents using piecewise variations along the pipe length. The major aim is to tailor the material distribution in the axial direction so as to avoid the occurrence of divergence instability without the penalty of increasing structural mass. Three types of boundary conditions have been examined; namely, Hinged-Hinged, Clamped- Hinged and Clamped-Clamped pipelines. The resulting optimization problem has been formulated as a nonlinear mathematical programming problem solved by invoking the MatLab optimization toolbox routines, which implement constrained function minimization routine named “fmincon" interacting with the associated eigenvalue problem routines. In fact, the proposed mathematical models have succeeded in maximizing the critical flow velocity without mass penalty and producing efficient and economic designs having enhanced stability characteristics as compared with the baseline designs.

Keywords: Functionally graded materials, pipe flow, optimumdesign, fluid- structure interaction

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

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Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Rabih Ghostine, Craig Kapfer, Viswanathan Kannan, Ibrahim Hoteit

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Urban flooding resulting from a sudden release of water due to dam-break or excessive rainfall is a serious threatening environment hazard, which causes loss of human life and large economic losses. Anticipating floods before they occur could minimize human and economic losses through the implementation of appropriate protection, provision, and rescue plans. This work reports on the numerical modelling of flash flood propagation in urban areas after an excessive rainfall event or dam-break. A two-dimensional (2D) depth-averaged shallow water model is used with a refined unstructured grid of triangles for representing the urban area topography. The 2D shallow water equations are solved using a second-order well-balanced discontinuous Galerkin scheme. Theoretical test case and three flood events are described to demonstrate the potential benefits of the scheme: (i) wetting and drying in a parabolic basin (ii) flash flood over a physical model of the urbanized Toce River valley in Italy; (iii) wave propagation on the Reyran river valley in consequence of the Malpasset dam-break in 1959 (France); and (iv) dam-break flood in October 1982 at the town of Sumacarcel (Spain). The capability of the scheme is also verified against alternative models. Computational results compare well with recorded data and show that the scheme is at least as efficient as comparable second-order finite volume schemes, with notable efficiency speedup due to parallelization.

Keywords: Flood modeling, dam-break, shallow water equations, Discontinuous Galerkin scheme, MUSCL scheme.

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Authors: Salem Alsanusi, Loubna Bentaher

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Response Surface Methods (RSM) provide statistically validated predictive models that can then be manipulated for finding optimal process configurations. Variation transmitted to responses from poorly controlled process factors can be accounted for by the mathematical technique of propagation of error (POE), which facilitates ‘finding the flats’ on the surfaces generated by RSM. The dual response approach to RSM captures the standard deviation of the output as well as the average. It accounts for unknown sources of variation. Dual response plus propagation of error (POE) provides a more useful model of overall response variation. In our case, we implemented this technique in predicting compressive strength of concrete of 28 days in age. Since 28 days is quite time consuming, while it is important to ensure the quality control process. This paper investigates the potential of using design of experiments (DOE-RSM) to predict the compressive strength of concrete at 28th day. Data used for this study was carried out from experiment schemes at university of Benghazi, civil engineering department. A total of 114 sets of data were implemented. ACI mix design method was utilized for the mix design. No admixtures were used, only the main concrete mix constituents such as cement, coarseaggregate, fine aggregate and water were utilized in all mixes. Different mix proportions of the ingredients and different water cement ratio were used. The proposed mathematical models are capable of predicting the required concrete compressive strength of concrete from early ages.

Keywords: Mix proportioning, response surface methodology, compressive strength, optimal design.

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Authors: Laor Boongasame, Veera Boonjing, Ho-fung Leung

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Buyer coalition with a combination of items is a group of buyers joining together to purchase a combination of items with a larger discount. The primary aim of existing buyer coalition with a combination of items research is to generate a large total discount. However, the aim is hard to achieve because this research is based on the assumption that each buyer completely knows other buyers- information or at least one buyer knows other buyers- information in a coalition by exchange of information. These assumption contrast with the real world environment where buyers join a coalition with incomplete information, i.e., they concerned only with their expected discounts. Therefore, this paper proposes a new buyer community coalition formation with a combination of items scheme, called the Community Compromised Combinatorial Coalition scheme, under such an environment of incomplete information. In order to generate a larger total discount, after buyers who want to join a coalition propose their minimum required saving, a coalition structure that gives a maximum total retail prices is formed. Then, the total discount division of the coalition is divided among buyers in the coalition depending on their minimum required saving and is a Pareto optimal. In mathematical analysis, we compare concepts of this scheme with concepts of the existing buyer coalition scheme. Our mathematical analysis results show that the total discount of the coalition in this scheme is larger than that in the existing buyer coalition scheme.

Keywords: group decision and negotiations, group buying, gametheory, combinatorial coalition formation, Pareto optimality

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Authors: Esam Jassim

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Industries using conventional fossil fuels have an  interest in better understanding the mechanism of particulate  formation during combustion since such is responsible for emission  of undesired inorganic elements that directly impact the atmospheric  pollution level. Fine and ultrafine particulates have tendency to  escape the flue gas cleaning devices to the atmosphere. They also  preferentially collect on surfaces in power systems resulting in  ascending in corrosion inclination, descending in the heat transfer  thermal unit, and severe impact on human health. This adverseness  manifests particularly in the regions of world where coal is the  dominated source of energy for consumption.  This study highlights the behavior of calcium transformation as  mineral grains verses organically associated inorganic components  during pulverized coal combustion. The influence of existing type of  calcium on the coarse, fine and ultrafine mode formation mechanisms  is also presented. The impact of two sub-bituminous coals on particle  size and calcium composition evolution during combustion is to be  assessed. Three mixed blends named Blends 1, 2, and 3 are selected  according to the ration of coal A to coal B by weight. Calcium  percentage in original coal increases as going from Blend 1 to 3.  A mathematical model and a new approach of describing  constituent distribution are proposed. Analysis of experiments of  calcium distribution in ash is also modeled using Poisson distribution.  A novel parameter, called elemental index λ, is introduced as a  measuring factor of element distribution.  Results show that calcium in ash that originally in coal as mineral  grains has index of 17, whereas organically associated calcium  transformed to fly ash shown to be best described when elemental  index λ is 7.  As an alkaline-earth element, calcium is considered the  fundamental element responsible for boiler deficiency since it is the  major player in the mechanism of ash slagging process. The  mechanism of particle size distribution and mineral species of ash  particles are presented using CCSEM and size-segregated ash  characteristics. Conclusions are drawn from the analysis of  pulverized coal ash generated from a utility-scale boiler.

 

Keywords: Calcium transformation, Coal Combustion, Inorganic Element, Poisson distribution.

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Authors: Theerawat Songyot, Tham Manjing, Bunyarit Uyyanonvara, Chanjira Sinthanayothin

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Corner detection and optical flow are common techniques for feature-based video stabilization. However, these algorithms are computationally expensive and should be performed at a reasonable rate. This paper presents an algorithm for discarding irrelevant feature points and maintaining them for future use so as to improve the computational cost. The algorithm starts by initializing a maintained set. The feature points in the maintained set are examined against its accuracy for modeling. Corner detection is required only when the feature points are insufficiently accurate for future modeling. Then, optical flows are computed from the maintained feature points toward the consecutive frame. After that, a motion model is estimated based on the simplified affine motion model and least square method, with outliers belonging to moving objects presented. Studentized residuals are used to eliminate such outliers. The model estimation and elimination processes repeat until no more outliers are identified. Finally, the entire algorithm repeats along the video sequence with the points remaining from the previous iteration used as the maintained set. As a practical application, an efficient video stabilization can be achieved by exploiting the computed motion models. Our study shows that the number of times corner detection needs to perform is greatly reduced, thus significantly improving the computational cost. Moreover, optical flow vectors are computed for only the maintained feature points, not for outliers, thus also reducing the computational cost. In addition, the feature points after reduction can sufficiently be used for background objects tracking as demonstrated in the simple video stabilizer based on our proposed algorithm.

Keywords: background object tracking, feature point reduction, low cost tracking, video stabilization.

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Authors: M. Daoudi, A. Belghachi, L. Varani

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In this paper, we analyze the problem of quasiballistic electron transport in ultra small of mercury -cadmiumtelluride (Hg0.8Cd0.2Te -MCT) n+-n- n+ devices from hydrodynamic point view. From our study, we note that, when the size of the active layer is low than 0.1μm and for low bias application( ( ≥ 9mV), the quasi-ballistic transport has an important effect.

Keywords: Hg0.8Cd0.2Te semiconductor, Hydrodynamicmode, Quasi-ballistic transport, Submicron diode

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Authors: T. Aboumahboub, R. Brecha, H. B. Shrestha, U. F. Hutfilter, A. Geiges, W. Hare, M. Schaeffer, L. Welder, M. Gidden

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The proposed stringent mitigation targets require an immediate start for a drastic transformation of the whole energy system. The current Australian energy system is mainly centralized and fossil fuel-based in most states with coal and gas-fired plants dominating the total produced electricity over the recent past. On the other hand, the country is characterized by a huge, untapped renewable potential, where wind and solar energy could play a key role in the decarbonization of the Australia’s future energy system. However, integrating high shares of such variable renewable energy sources (VRES) challenges the power system considerably due to their temporal fluctuations and geographical dispersion. This raises the concerns about flexibility gap in the system to ensure the security of supply with increasing shares of such intermittent sources. One main flexibility dimension to facilitate system integration of high shares of VRES is to increase the cross-sectoral integration through coupling of electricity to other energy sectors alongside the decarbonization of the power sector and reinforcement of the transmission grid. This paper applies a multi-sectoral energy system optimization model for Australia. We investigate the cost-optimal configuration of a renewable-based Australian energy system and its transformation pathway in line with the ambitious range of proposed climate change mitigation targets. We particularly analyse the implications of linking the electricity and transport sectors in a prospective, highly renewable Australian energy system.

Keywords: Decarbonization, energy system modeling, sector coupling, variable renewable energies.

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Authors: Mohammed El Raey, Moustafa Osman Mohammed

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The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. The plant safety parameters are identified for engineering topology as employed in assessment safety of industrial ecology. In particular, the most severe accidental release of hazardous gaseous is postulated, analyzed and assessment in industrial region. The IAEA-safety assessment procedure is used to account the duration and rate of discharge of liquid chlorine. The ecological model of plume dispersion width and concentration of chlorine gas in the downwind direction is determined using Gaussian Plume Model in urban and rural areas and presented with SURFER®. The prediction of accident consequences is traced in risk contour concentration lines. The local greenhouse effect is predicted with relevant conclusions. The spatial-ecological model is predicted for multiple factors distribution schemes of multi-criteria analysis. The input–output analysis is explored from the spillover effect, and we conducted Monte Carlo simulations for sensitivity analysis. Their unique structure is balanced within “equilibrium patterns”, such as the composite index for biosphere with collective structure of many distributed feedback flows. These dynamic structures are related to have their physical and chemical properties and enable a gradual and prolonged incremental pattern. While this spatial model structure argues from ecology, resource savings, static load design, financial and other pragmatic reasons, the outcomes are not decisive in an artistic/architectural perspective. The hypothesis is deployed to unify analytic and analogical spatial structure in development urban environments using optimization loads as an example of integrated industrial structure where the process is based on engineering topology of systems ecology.

Keywords: Spatial-ecological modeling, spatial structure orientation impact, composite structure, industrial ecology.

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Authors: T. C. Manjunath, B. Bandyopadhyay

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Active vibration control is an important problem in structures. The objective of active vibration control is to reduce the vibrations of a system by automatic modification of the system-s structural response. In this paper, the modeling and design of a fast output sampling feedback controller for a smart flexible beam system embedded with shear sensors and actuators for SISO system using Timoshenko beam theory is proposed. FEM theory, Timoshenko beam theory and the state space techniques are used to model the aluminum cantilever beam. For the SISO case, the beam is divided into 5 finite elements and the control actuator is placed at finite element position 1, whereas the sensor is varied from position 2 to 5, i.e., from the nearby fixed end to the free end. Controllers are designed using FOS method and the performance of the designed FOS controller is evaluated for vibration control for 4 SISO models of the same plant. The effect of placing the sensor at different locations on the beam is observed and the performance of the controller is evaluated for vibration control. Some of the limitations of the Euler-Bernoulli theory such as the neglection of shear and axial displacement are being considered here, thus giving rise to an accurate beam model. Embedded shear sensors and actuators have been considered in this paper instead of the surface mounted sensors and actuators for vibration suppression because of lot of advantages. In controlling the vibration modes, the first three dominant modes of vibration of the system are considered.

Keywords: Smart structure, Timoshenko beam theory, Fast output sampling feedback control, Finite Element Method, State space model, SISO, Vibration control, LMI

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Authors: Daehwan Kim, Jaemin Kim, Seongwon Cho, Yongsuk Jang, Sun-Tae Chung, Boo-Gyoun Kim

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AAM has been successfully applied to face alignment, but its performance is very sensitive to initial values. In case the initial values are a little far distant from the global optimum values, there exists a pretty good possibility that AAM-based face alignment may converge to a local minimum. In this paper, we propose a progressive AAM-based face alignment algorithm which first finds the feature parameter vector fitting the inner facial feature points of the face and later localize the feature points of the whole face using the first information. The proposed progressive AAM-based face alignment algorithm utilizes the fact that the feature points of the inner part of the face are less variant and less affected by the background surrounding the face than those of the outer part (like the chin contour). The proposed algorithm consists of two stages: modeling and relation derivation stage and fitting stage. Modeling and relation derivation stage first needs to construct two AAM models: the inner face AAM model and the whole face AAM model and then derive relation matrix between the inner face AAM parameter vector and the whole face AAM model parameter vector. In the fitting stage, the proposed algorithm aligns face progressively through two phases. In the first phase, the proposed algorithm will find the feature parameter vector fitting the inner facial AAM model into a new input face image, and then in the second phase it localizes the whole facial feature points of the new input face image based on the whole face AAM model using the initial parameter vector estimated from using the inner feature parameter vector obtained in the first phase and the relation matrix obtained in the first stage. Through experiments, it is verified that the proposed progressive AAM-based face alignment algorithm is more robust with respect to pose, illumination, and face background than the conventional basic AAM-based face alignment algorithm.

Keywords: Face Alignment, AAM, facial feature detection, model matching.

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Authors: M. N. Zelenin, V. S. Mikhailov, R. P. Zhivotovsky

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It is known that residual welding deformations give negative effect to processability and operational quality of welded structures, complicating their assembly and reducing strength. Therefore, selection of optimal technology, ensuring minimum welding deformations, is one of the main goals in developing a technology for manufacturing of welded structures. Through years, JSC SSTC has been developing a theory for estimation of welding deformations and practical activities for reducing and compensating such deformations during welding process. During long time a methodology was used, based on analytic dependence. This methodology allowed defining volumetric changes of metal due to welding heating and subsequent cooling. However, dependences for definition of structures deformations, arising as a result of volumetric changes of metal in the weld area, allowed performing calculations only for simple structures, such as units, flat sections and sections with small curvature. In case of complex 3D structures, estimations on the base of analytic dependences gave significant errors. To eliminate this shortage, it was suggested to use finite elements method for resolving of deformation problem. Here, one shall first calculate volumes of longitudinal and transversal shortenings of welding joints using method of analytic dependences and further, with obtained shortenings, calculate forces, which action is equivalent to the action of active welding stresses. Further, a finiteelements model of the structure is developed and equivalent forces are added to this model. Having results of calculations, an optimal sequence of assembly and welding is selected and special measures to reduce and compensate welding deformations are developed and taken.

Keywords: Finite elements method, modeling, expected welding deformations, welding, assembling.

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Authors: T.C. Manjunath, B. Bandyopadhyay

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This paper features the modeling and design of a Robust Decentralized Fast Output Sampling (RDFOS) Feedback control technique for the active vibration control of a smart flexible multimodel Euler-Bernoulli cantilever beams for a multivariable (MIMO) case by retaining the first 6 vibratory modes. The beam structure is modeled in state space form using the concept of piezoelectric theory, the Euler-Bernoulli beam theory and the Finite Element Method (FEM) technique by dividing the beam into 4 finite elements and placing the piezoelectric sensor / actuator at two finite element locations (positions 2 and 4) as collocated pairs, i.e., as surface mounted sensor / actuator, thus giving rise to a multivariable model of the smart structure plant with two inputs and two outputs. Five such multivariable models are obtained by varying the dimensions (aspect ratios) of the aluminium beam. Using model order reduction technique, the reduced order model of the higher order system is obtained based on dominant Eigen value retention and the Davison technique. RDFOS feedback controllers are designed for the above 5 multivariable-multimodel plant. The closed loop responses with the RDFOS feedback gain and the magnitudes of the control input are obtained and the performance of the proposed multimodel smart structure system is evaluated for vibration control.

Keywords: Smart structure, Euler-Bernoulli beam theory, Fastoutput sampling feedback control, Finite Element Method, Statespace model, Vibration control, LMI, Model order Reduction.

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Authors: Raja Singh Khela, R. K. Bansal, K. S. Sandhu, A. K. Goel

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Self-Excited Induction Generator (SEIG) builds up voltage while it enters in its magnetic saturation region. Due to non-linear magnetic characteristics, the performance analysis of SEIG involves cumbersome mathematical computations. The dependence of air-gap voltage on saturated magnetizing reactance can only be established at rated frequency by conducting a laboratory test commonly known as synchronous run test. But, there is no laboratory method to determine saturated magnetizing reactance and air-gap voltage of SEIG at varying speed, terminal capacitance and other loading conditions. For overall analysis of SEIG, prior information of magnetizing reactance, generated frequency and air-gap voltage is essentially required. Thus, analytical methods are the only alternative to determine these variables. Non-existence of direct mathematical relationship of these variables for different terminal conditions has forced the researchers to evolve new computational techniques. Artificial Neural Networks (ANNs) are very useful for solution of such complex problems, as they do not require any a priori information about the system. In this paper, an attempt is made to use cascaded neural networks to first determine the generated frequency and magnetizing reactance with varying terminal conditions and then air-gap voltage of SEIG. The results obtained from the ANN model are used to evaluate the overall performance of SEIG and are found to be in good agreement with experimental results. Hence, it is concluded that analysis of SEIG can be carried out effectively using ANNs.

Keywords: Self-Excited Induction Generator, Artificial NeuralNetworks, Exciting Capacitance and Saturated magnetizingreactance.

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Authors: B. Mukanova, N. Glazyrina, S. Glazyrin

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The formulated problem of optimization of the technological process of water treatment for thermal power plants is considered in this article. The problem is of multiparametric nature. To optimize the process, namely, reduce the amount of waste water, a new technology was developed to reuse such water. A mathematical model of the technology of wastewater reuse was developed. Optimization parameters were determined. The model consists of a material balance equation, an equation describing the kinetics of ion exchange for the non-equilibrium case and an equation for the ion exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion exchange. A direct problem of calculating the impurity concentration at the outlet of the water treatment plant was numerically solved. The direct problem was approximated by an implicit point-to-point computation difference scheme. The inverse problem was formulated as relates to determination of the parameters of the mathematical model of the water treatment plant operating in non-equilibrium conditions. The formulated inverse problem was solved. Following the results of calculation the time of start of the filter regeneration process was determined, as well as the period of regeneration process and the amount of regeneration and wash water. Multi-parameter optimization of water treatment process for thermal power plants allowed decreasing the amount of wastewater by 15%.

Keywords: Direct problem, multiparametric optimization, optimization parameters, water treatment.

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Authors: M. Seguini, D. Nedjar

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An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.

Keywords: Finite element method, geometric nonlinearity, material nonlinearity, soil-structure interaction, spatial variability.

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: Deep neural models, natural language inference, recognizing textual entailment, sentence-to-sentence relation.

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Authors: Shu-Hui Jan, Hui-Ping Tserng, Shih-Ping Ho

Abstract:

Construction project control attempts to obtain real-time as-built schedule information and to eliminate project delays by effectively enhancing dynamic schedule control and management. Suitable platforms for enhancing an as-built schedule visually during the construction phase are necessary and important for general contractors. As the application of building information modeling (BIM) becomes more common, schedule management integrated with the BIM approach becomes essential to enhance visual construction management implementation for the general contractor during the construction phase. To enhance visualization of the updated as-built schedule for the general contractor, this study presents a novel system called the Construction BIM-assisted Schedule Management (ConBIM-SM) system for general contractors in Taiwan. The primary purpose of this study is to develop a web ConBIM-SM system for the general contractor to enhance visual as-built schedule information sharing and efficiency in tracking construction as-built schedule. Finally, the ConBIM-SM system is applied to a case study of a commerce building project in Taiwan to verify its efficacy and demonstrate its effectiveness during the construction phase. The advantages of the ConBIM-SM system lie in improved project control and management efficiency for general contractors, and in providing BIM-assisted as-built schedule tracking and management, to access the most current as-built schedule information through a web browser. The case study results show that the ConBIM-SM system is an effective visual as-built schedule management platform integrated with the BIM approach for general contractors in a construction project.

Keywords: BIM, Building information modeling, construction schedule management, as-built schedule management, BIM schedule updating mechanism.

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Authors: Tarek M. Alguhane, Ayman H. Khalil, M. N. Fayed, Ayman M. Ismail

Abstract:

This paper deals with different modeling aspects of masonry infill: no infill model, Layered shell infill model, and strut infill model. These models consider the complicated behavior of the in-filled plane frames under lateral load similar to an earthquake load. Three strut infill models are used: NBCC (2005) strut infill model, ASCE/SEI 41-06 strut infill model and proposed strut infill model based on modification to Canadian, NBCC (2005) strut infill model. Pushover and modal analyses of a masonry infill concrete frame with a single storey and an existing 5-storey RC building have been carried out by using different models for masonry infill. The corresponding hinge status, the value of base shear at target displacement as well as their dynamic characteristics have been determined and compared. A validation of the structural numerical models for the existing 5-storey RC building has been achieved by comparing the experimentally measured and the analytically estimated natural frequencies and their mode shapes. This study shows that ASCE/SEI 41-06 equation underestimates the values for the equivalent properties of the diagonal strut while Canadian, NBCC (2005) equation gives realistic values for the equivalent properties. The results indicate that both ASCE/SEI 41-06 and Canadian, NBCC (2005) equations for strut infill model give over estimated values for dynamic characteristic of the building. Proposed modification to Canadian, NBCC (2005) equation shows that the fundamental dynamic characteristic values of the building are nearly similar to the corresponding values using layered shell elements as well as measured field results.

Keywords: Masonry infill, framed structures, RC buildings, non-structural elements.

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Authors: Pakawhat Khumkhreung, Yottana Khunatorn

Abstract:

The flow pattern inside rectangular intake air duct of 300 MW lignite coal-fired power plant is investigated in order to analyze and reduce overall inlet system pressure drop. The system consists of the 45-degree inlet elbow, the flow instrument, the 90-degree mitered elbow and fans, respectively. The energy loss in each section can be determined by Bernoulli’s equation and ASHRAE standard table. Hence, computational fluid dynamics (CFD) is used in this study based on Navier-Stroke equation and the standard k-epsilon turbulence modeling. Input boundary condition is 175 kg/s mass flow rate inside the 11-m² cross sectional duct. According to the inlet air flow rate, the Reynolds number of airstream is 2.7x10⁶ (based on the hydraulic duct diameter), thus the flow behavior is turbulence. The numerical results are validated with the real operation data. It is found that the numerical result agrees well with the operating data, and dominant loss occurs at the flow rate measurement device. Normally, the air flow rate is measured by the airfoil and it gets high pressure drop inside the duct. To overcome this problem, the airfoil is planned to be replaced with the other type measuring instrument, such as the average pitot tube which generates low pressure drop of airstream. The numerical result in case of average pitot tube shows that the pressure drop inside the inlet airstream duct is decreased significantly. It should be noted that the energy consumption of inlet air system is reduced too.

Keywords: Airfoil, average pitot tube, combustion air, CFD, pressure drop, rectangular duct.

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Authors: A. Elakkary, A. Echchatbi, N. Elalami

Abstract:

In this paper, we are interested in attitude control of a satellite, which using wheels of reaction, by state feedback. First, we develop a method allowing us to put the control and its integral in the state-feedback form. Then, by using the theorem of Gronwall- Bellman, we put the sufficient conditions so that the nonlinear system modeling the satellite is stabilisable and observed by state feedback.

Keywords: Satellite, attitude control, state feedback, attitude stabilization.

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Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

Abstract:

A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

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Authors: Ana Gabriela Diaz, Luis Fermín Córdova, Jr., Roberto Lamazares

Abstract:

The island of Cuba, the largest of the Greater Antilles, is located in the tropical North Atlantic. It is annually affected by numerous weather events, which have caused severe damage to our coastal areas. In the same way that many other coastlines around the world, the beautiful beaches of the Hicacos Peninsula also suffer from erosion. This leads to a structural regression of the coastline. If measures are not taken, the hotels will be exposed to the advance of the sea, and it will be a serious problem for the economy. With the aim of studying the intensity of this type of activity, specialists of group of coastal and marine engineering from CIH, in the framework of the research conducted within the project MEGACOSTAS 2, provide their research to simulate extreme events and assess their impact in coastal areas, mainly regarding the definition of flood volumes and morphodynamic changes in sandy beaches. The main objective of this work is the evaluation of the process of Varadero beach erosion (the coastal sector has an important impact in the country's economy) on the Hicacos Peninsula for different paths of hurricanes. The mathematical model XBeach, which was integrated into the Coastal engineering system introduced by the project of MEGACOSTA 2 to determine the area and the more critical profiles for the path of hurricanes under study, was applied. The results of this project have shown that Center area is the greatest dynamic area in the simulation of the three paths of hurricanes under study, showing high erosion volumes and the greatest average length of regression of the coastline, from 15- 22 m.

Keywords: Beach, erosion, mathematical model, coastal areas.

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Authors: Kaylash Chaudhary, Ansgar Fehnker

Abstract:

There are many virtual payment systems available to conduct micropayments. It is essential that the protocols satisfy the highest standards of correctness. This paper examines the Netpay Protocol [3], provide its formalization as automata model, and prove two important correctness properties, namely absence of deadlock and validity of an ecoin during the execution of the protocol. This paper assumes a cooperative customer and will prove that the protocol is executing according to its description.

Keywords: Model, Verification, Micropayment.

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Authors: E. Yosry, W. Fikry, A. El-henawy, M. Marzouk

Abstract:

This work offers a study of new simple compact model of dual-drain Magnetic Field Effect Transistor (MAGFET) including geometrical effects and biasing dependency. An explanation of the sensitivity is investigated, involving carrier deflection as the dominant operating principle. Finally, model verification with simulation results is introduced to ensure that acceptable error of 2% is achieved.

Keywords: MAGFET, Modeling, Simulation, Split-drain.

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